Pregled bibliografske jedinice broj: 1038942
Crop Classification using Multi-spectral and Multitemporal Satellite Imagery with Machine Learning
Crop Classification using Multi-spectral and Multitemporal Satellite Imagery with Machine Learning // Proceedings of International Conference on Software, Telecommunications and Computer Networks (SoftCOM 2019)
Split, Hrvatska, 2019. str. - doi:10.23919/SOFTCOM.2019.8903738 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
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Naslov
Crop Classification using Multi-spectral and Multitemporal Satellite Imagery with Machine Learning
Autori
Visković, Lucija ; Nižetić Kosović, Ivana ; Mastelić, Toni
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceedings of International Conference on Software, Telecommunications and Computer Networks (SoftCOM 2019)
/ - , 2019
Skup
2019 International Conference on Software, Telecommunications and Computer Networks (SoftCOM)
Mjesto i datum
Split, Hrvatska, 19.07.2019. - 21.07.2019
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
remote sensing ; satellite images ; land usage ; crop classification ; machine learning
Sažetak
Satellite images are highly utilized for detecting land usage, while in recent years a finer-grade crop classification has become important in the context of precision agriculture. However, such classification brings new challenges, which aside from multi- spectral images require exploitation of their multi-temporal properties as well, with pixel- based analysis and larger number of classes. In this paper, we apply several machine learning algorithms on multi-spectral and multi-temporal satellite images and derive crop classification models. The models are applied only on agricultural fields, which can be singled out with the existing land usage classification models. Results show that the random forest outperforms other algorithms with accuracy score of 0.8420 and Kappa score of 0.8157. Detailed analysis of recall and precision scores is given for each crop separately, followed by a comprehensive discussion.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo
POVEZANOST RADA
Ustanove:
Ericsson Nikola Tesla d.d.